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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code>.SparseCoder</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-decomposition-sparsecoder">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.decomposition.SparseCoder</span></code></a></li>
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  <div class="section" id="sklearn-decomposition-sparsecoder">
<h1><a class="reference internal" href="../classes.html#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a>.SparseCoder<a class="headerlink" href="#sklearn-decomposition-sparsecoder" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.decomposition.SparseCoder">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.decomposition.</code><code class="sig-name descname">SparseCoder</code><span class="sig-paren">(</span><em class="sig-param">dictionary</em>, <em class="sig-param">transform_algorithm='omp'</em>, <em class="sig-param">transform_n_nonzero_coefs=None</em>, <em class="sig-param">transform_alpha=None</em>, <em class="sig-param">split_sign=False</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">positive_code=False</em>, <em class="sig-param">transform_max_iter=1000</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L935"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder" title="Permalink to this definition">¶</a></dt>
<dd><p>Sparse coding</p>
<p>Finds a sparse representation of data against a fixed, precomputed
dictionary.</p>
<p>Each row of the result is the solution to a sparse coding problem.
The goal is to find a sparse array <code class="docutils literal notranslate"><span class="pre">code</span></code> such that:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">~=</span> <span class="n">code</span> <span class="o">*</span> <span class="n">dictionary</span>
</pre></div>
</div>
<p>Read more in the <a class="reference internal" href="../decomposition.html#sparsecoder"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>dictionary</strong><span class="classifier">array, [n_components, n_features]</span></dt><dd><p>The dictionary atoms used for sparse coding. Lines are assumed to be
normalized to unit norm.</p>
</dd>
<dt><strong>transform_algorithm</strong><span class="classifier">{‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’,     ‘threshold’}, default=’omp’</span></dt><dd><p>Algorithm used to transform the data:
lars: uses the least angle regression method (linear_model.lars_path)
lasso_lars: uses Lars to compute the Lasso solution
lasso_cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). lasso_lars will be faster if
the estimated components are sparse.
omp: uses orthogonal matching pursuit to estimate the sparse solution
threshold: squashes to zero all coefficients less than alpha from
the projection <code class="docutils literal notranslate"><span class="pre">dictionary</span> <span class="pre">*</span> <span class="pre">X'</span></code></p>
</dd>
<dt><strong>transform_n_nonzero_coefs</strong><span class="classifier">int, default=0.1*n_features</span></dt><dd><p>Number of nonzero coefficients to target in each column of the
solution. This is only used by <code class="docutils literal notranslate"><span class="pre">algorithm='lars'</span></code> and <code class="docutils literal notranslate"><span class="pre">algorithm='omp'</span></code>
and is overridden by <code class="docutils literal notranslate"><span class="pre">alpha</span></code> in the <code class="docutils literal notranslate"><span class="pre">omp</span></code> case.</p>
</dd>
<dt><strong>transform_alpha</strong><span class="classifier">float, default=1.</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">algorithm='lasso_lars'</span></code> or <code class="docutils literal notranslate"><span class="pre">algorithm='lasso_cd'</span></code>, <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is the
penalty applied to the L1 norm.
If <code class="docutils literal notranslate"><span class="pre">algorithm='threshold'</span></code>, <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is the absolute value of the
threshold below which coefficients will be squashed to zero.
If <code class="docutils literal notranslate"><span class="pre">algorithm='omp'</span></code>, <code class="docutils literal notranslate"><span class="pre">alpha</span></code> is the tolerance parameter: the value of
the reconstruction error targeted. In this case, it overrides
<code class="docutils literal notranslate"><span class="pre">n_nonzero_coefs</span></code>.</p>
</dd>
<dt><strong>split_sign</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to split the sparse feature vector into the concatenation of
its negative part and its positive part. This can improve the
performance of downstream classifiers.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>Number of parallel jobs to run.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>positive_code</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to enforce positivity when finding the code.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
<dt><strong>transform_max_iter</strong><span class="classifier">int, default=1000</span></dt><dd><p>Maximum number of iterations to perform if <code class="docutils literal notranslate"><span class="pre">algorithm='lasso_cd'</span></code> or
<code class="docutils literal notranslate"><span class="pre">lasso_lars</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>components_</strong><span class="classifier">array, [n_components, n_features]</span></dt><dd><p>The unchanged dictionary atoms</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.decomposition.DictionaryLearning.html#sklearn.decomposition.DictionaryLearning" title="sklearn.decomposition.DictionaryLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DictionaryLearning</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.decomposition.MiniBatchDictionaryLearning.html#sklearn.decomposition.MiniBatchDictionaryLearning" title="sklearn.decomposition.MiniBatchDictionaryLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MiniBatchDictionaryLearning</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA" title="sklearn.decomposition.SparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SparsePCA</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.decomposition.MiniBatchSparsePCA.html#sklearn.decomposition.MiniBatchSparsePCA" title="sklearn.decomposition.MiniBatchSparsePCA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MiniBatchSparsePCA</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="sklearn.decomposition.sparse_encode.html#sklearn.decomposition.sparse_encode" title="sklearn.decomposition.sparse_encode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse_encode</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.decomposition.SparseCoder.fit" title="sklearn.decomposition.SparseCoder.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Do nothing and return the estimator unchanged</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.decomposition.SparseCoder.fit_transform" title="sklearn.decomposition.SparseCoder.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.decomposition.SparseCoder.get_params" title="sklearn.decomposition.SparseCoder.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.decomposition.SparseCoder.set_params" title="sklearn.decomposition.SparseCoder.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.decomposition.SparseCoder.transform" title="sklearn.decomposition.SparseCoder.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Encode the data as a sparse combination of the dictionary atoms.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.decomposition.SparseCoder.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">dictionary</em>, <em class="sig-param">transform_algorithm='omp'</em>, <em class="sig-param">transform_n_nonzero_coefs=None</em>, <em class="sig-param">transform_alpha=None</em>, <em class="sig-param">split_sign=False</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">positive_code=False</em>, <em class="sig-param">transform_max_iter=1000</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L1017"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.SparseCoder.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L1028"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Do nothing and return the estimator unchanged</p>
<p>This method is just there to implement the usual API and hence
work in pipelines.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">Ignored</span></dt><dd></dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd></dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns the object itself</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.SparseCoder.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.SparseCoder.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.SparseCoder.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.decomposition.SparseCoder.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/decomposition/_dict_learning.py#L896"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.decomposition.SparseCoder.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Encode the data as a sparse combination of the dictionary atoms.</p>
<p>Coding method is determined by the object parameter
<code class="docutils literal notranslate"><span class="pre">transform_algorithm</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape (n_samples, n_features)</span></dt><dd><p>Test data to be transformed, must have the same number of
features as the data used to train the model.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">array, shape (n_samples, n_components)</span></dt><dd><p>Transformed data</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-decomposition-sparsecoder">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.decomposition.SparseCoder</span></code><a class="headerlink" href="#examples-using-sklearn-decomposition-sparsecoder" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Transform a signal as a sparse combination of Ricker wavelets. This example visually compares d..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_sparse_coding_thumb.png" src="../../_images/sphx_glr_plot_sparse_coding_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/decomposition/plot_sparse_coding.html#sphx-glr-auto-examples-decomposition-plot-sparse-coding-py"><span class="std std-ref">Sparse coding with a precomputed dictionary</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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